Create a scatter plot from data points (requires matplotlib). Examples: plot_scatter([1, 2, 3, 4], [1, 4, 9, 16], title="Correlation Study") plot_scatter([1, 2, 3], [2, 4, 5], color='purple', point_size=100)
Part of the Math MCP Learning server.
Free to start. No card required.
AI agents use plot_scatter to create or modify resources in Math MCP Learning. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.
Without a policy, an AI agent could call plot_scatter repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach Math MCP Learning.
Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.
{
"version": "1",
"default": "deny",
"tools": {
"plot_scatter": {
"limits": [
{
"counter": "plot_scatter_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}See the full Math MCP Learning policy for all 17 tools.
These attack patterns abuse exactly the kind of access plot_scatter gives an agent. Each links to the full case and the policy that stops it:
Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Create a scatter plot from data points (requires matplotlib). Examples: plot_scatter([1, 2, 3, 4], [1, 4, 9, 16], title="Correlation Study") plot_scatter([1, 2, 3], [2, 4, 5], color='purple', point_size=100). It is categorised as a Write tool in the Math MCP Learning MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Math MCP Learning MCP server in PolicyLayer and add a rule for plot_scatter: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Math MCP Learning. Nothing to install.
plot_scatter is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the plot_scatter rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for plot_scatter. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
plot_scatter is provided by the Math MCP Learning MCP server (pypi:math-mcp-learning-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 17 Math MCP Learning tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
4,600+ MCP servers and 31,000+ tools scanned and risk-classified.